This is a second course in the topics of pure mathematics, particularly those most commonly used in the study of computing science and related applications. It includes proof techniques, models of computation, formal languages, analysis of algorithms, trees and advanced general graph theory with applications, finite state and automata theory, encryption, and an elementary introduction to mathematical structures such as groups, rings, and fields.

This course is designed to give students the ability to analyze, formulate, and program problems related to discrete simulation methods. The course introduces students to queuing theory and some commonly used continuous and discrete statistical distributions. By the end of the course, students are able to simulate real world computer systems and industrial manufacturing systems.

The history, development, and design principles for programming notations. The design and internal operations of the major notational categories are examined in detail. Students are expected to become proficient in at least four languages they have not previously learned, typically chosen from historical, modern working, and cutting edge languages and from among such (non-exclusive) categories as Algol-descended, functional, scripting, Web-based, modular, application-specific, visual, and object oriented. They will also learn how to select appropriate programming notations for a given project. Programming will be undertaken in at least three OS environments.

This course introduces the fundamentals of computer graphics and principles of raster image generation. Topics include: graphics primitives, coordinate systems, transformations, rendering techniques, and geometric modelling.

This course provides a general introduction to interaction design from a human-computer interaction perspective. Students will learn both theoretical and practical concepts of human-computer interaction which will help them discover requirements, design/prototype and evaluate interactive products with usability and user experience (UX) goals. The course covers human capabilities, design principles, prototyping techniques, implementation, and evaluation techniques for interactive products. Students will apply what they learn from lectures to actual challenges of interactive product design, prototyping, implementation, and evaluation.

Artificial Intelligence: knowledge representation, logic programming, knowledge inference. Application domains within the discipline of Artificial Intelligence include logical and probabilistic reasoning, natural language understanding, vision and expert systems.

An overview of core machine learning technologies with motivating applications from a variety of disciplines and real‐world data sets. Students will learn how to implement, evaluate, and improve machine learning algorithms. While studying best practice in machine learning, students are introduced to data mining and statistical pattern recognition and learn how to build automatic analytical models. Topics include identification and extraction of useful features that best represent available data, some of the most important machine learning algorithms, and the evaluation of algorithm performance.

An introduction to the theory of designing and carrying out large software projects. All stages of the software engineering cycle, including requirement analysis, design, implementation, testing, and maintenance will be examined. The student will complete a semester-long team based project.

This course will study advanced techniques, tools, and standards in software engineering. The student will complete a semester-long team based project